Spatial-omics approaches to dissect host-disease interaction
Abstract/Contents
- Abstract
- The spatial interaction between different biological agents and the host immune system plays a critical role in maintaining tissue homeostasis and influencing disease outcomes. These interactions can be characterized by their complexity and diversity, encompassing various scenarios: Harmonized Interaction, for example, the mutualistic relationship between the microbiota and the immune system is vital for maintaining tissue integrity and immune homeostasis. Hostile Interaction, for example when a viral infection occurs, it triggers a local immune response aimed at eliminating the pathogen. This immune response can result in systemic retaliation, leading to inflammation and other systemic effects. Dynamic Interaction, for example within the tumor microenvironment, there exists a dynamic interplay between tumor cells and immune cells. This complex interaction influences the efficacy of immunotherapy and can determine the outcome of the disease. However, studying these interactions requires methodologies that go beyond traditional single-domain approaches. To comprehensively understand these complex phenomena, we need methods that can capture and preserve spatio-temporal information, identify cross-domain feature responses in a high-plex format, and harmonize biological knowledge across different modalities and biological entities. This thesis work attempts to bridge this gap. Chapter 1 provides a background on tissue biology and the methodologies developed so far, and the unmet need in this field. Chapter 2 introduces MicroCart, an experimental assay for studying host-microbiome interactions, highlighting its design, validation process, and compatibility with different modalities. Chapter 3 presents Redsea, a computational method addressing lateral signal contamination in imaging, emphasizing its significance in improving imaging quality. Chapter 4 discusses Mario, a data integration algorithm enabling the matching and integration of data from different single-cell proteomic and multi-modal methods. Chapter 5 focuses on MaxFuse, a data integration algorithm designed for 'weakly-linked' modalities, facilitating the creation of a spatial-trimodality dataset, showcasing its benefits and applications.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Zhu, Bokai |
|---|---|
| Degree supervisor | Nolan, Garry P |
| Thesis advisor | Nolan, Garry P |
| Thesis advisor | D'Angelo, Robert |
| Thesis advisor | Gaudilliere, Brice |
| Thesis advisor | Huang, Kerwyn Casey, 1979- |
| Degree committee member | D'Angelo, Robert |
| Degree committee member | Gaudilliere, Brice |
| Degree committee member | Huang, Kerwyn Casey, 1979- |
| Associated with | Stanford University, School of Medicine |
| Associated with | Stanford University, Department of Microbiology and Immunology |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Bokai Zhu. |
|---|---|
| Note | Submitted to the Department of Microbiology and Immunology. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/sq343gt8059 |
Access conditions
- Copyright
- © 2023 by Bokai Zhu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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